{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "import torchvision.models as models\n",
    "import torchvision.transforms as transforms\n",
    "from torchvision.transforms import RandAugment\n",
    "from torch.optim import lr_scheduler\n",
    "import numpy as np\n",
    "import time\n",
    "import copy\n",
    "import matplotlib.pyplot as plt\n",
    "from HighwayNet import *\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n",
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)\n",
    " ## 修改模型\n",
    "net = HighwayNet().to(device)\n",
    "num_epochs=50\n",
    " # 定义损失函数和优化器\n",
    "trainloader,testloader = get_data_loader()\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)\n",
    "scheduler = lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.1)\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 0/49 轮训练\n",
      "----------\n",
      "train 损失: 4.1208 Top-1 准确率: 0.0670 Top-5 准确率: 0.2318\n",
      "val 损失: 4.0711 Top-1 准确率: 0.0785 Top-5 准确率: 0.2565\n",
      "\n",
      "第 1/49 轮训练\n",
      "----------\n",
      "train 损失: 3.5609 Top-1 准确率: 0.1500 Top-5 准确率: 0.4052\n",
      "val 损失: 3.6772 Top-1 准确率: 0.1473 Top-5 准确率: 0.3900\n",
      "\n",
      "第 2/49 轮训练\n",
      "----------\n",
      "train 损失: 3.0394 Top-1 准确率: 0.2399 Top-5 准确率: 0.5469\n",
      "val 损失: 3.4113 Top-1 准确率: 0.1758 Top-5 准确率: 0.4778\n",
      "\n",
      "第 3/49 轮训练\n",
      "----------\n",
      "train 损失: 2.6567 Top-1 准确率: 0.3131 Top-5 准确率: 0.6406\n",
      "val 损失: 2.8570 Top-1 准确率: 0.2807 Top-5 准确率: 0.5939\n",
      "\n",
      "第 4/49 轮训练\n",
      "----------\n",
      "train 损失: 2.3515 Top-1 准确率: 0.3779 Top-5 准确率: 0.7074\n",
      "val 损失: 2.3736 Top-1 准确率: 0.3777 Top-5 准确率: 0.6969\n",
      "\n",
      "第 5/49 轮训练\n",
      "----------\n",
      "train 损失: 2.1320 Top-1 准确率: 0.4279 Top-5 准确率: 0.7512\n",
      "val 损失: 2.4135 Top-1 准确率: 0.3790 Top-5 准确率: 0.6991\n",
      "\n",
      "第 6/49 轮训练\n",
      "----------\n",
      "train 损失: 1.9667 Top-1 准确率: 0.4675 Top-5 准确率: 0.7831\n",
      "val 损失: 2.3046 Top-1 准确率: 0.4060 Top-5 准确率: 0.7129\n",
      "\n",
      "第 7/49 轮训练\n",
      "----------\n",
      "train 损失: 1.8255 Top-1 准确率: 0.5001 Top-5 准确率: 0.8094\n",
      "val 损失: 3.0333 Top-1 准确率: 0.3336 Top-5 准确率: 0.6455\n",
      "\n",
      "第 8/49 轮训练\n",
      "----------\n",
      "train 损失: 1.7260 Top-1 准确率: 0.5224 Top-5 准确率: 0.8274\n",
      "val 损失: 1.9197 Top-1 准确率: 0.4858 Top-5 准确率: 0.7983\n",
      "\n",
      "第 9/49 轮训练\n",
      "----------\n",
      "train 损失: 1.6286 Top-1 准确率: 0.5488 Top-5 准确率: 0.8419\n",
      "val 损失: 1.9792 Top-1 准确率: 0.4764 Top-5 准确率: 0.7789\n",
      "\n",
      "第 10/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5346 Top-1 准确率: 0.5707 Top-5 准确率: 0.8574\n",
      "val 损失: 1.8249 Top-1 准确率: 0.4994 Top-5 准确率: 0.8111\n",
      "\n",
      "第 11/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4628 Top-1 准确率: 0.5885 Top-5 准确率: 0.8684\n",
      "val 损失: 1.7904 Top-1 准确率: 0.5198 Top-5 准确率: 0.8149\n",
      "\n",
      "第 12/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3927 Top-1 准确率: 0.6061 Top-5 准确率: 0.8785\n",
      "val 损失: 1.7741 Top-1 准确率: 0.5244 Top-5 准确率: 0.8267\n",
      "\n",
      "第 13/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3423 Top-1 准确率: 0.6193 Top-5 准确率: 0.8849\n",
      "val 损失: 1.8426 Top-1 准确率: 0.5113 Top-5 准确率: 0.8150\n",
      "\n",
      "第 14/49 轮训练\n",
      "----------\n",
      "train 损失: 1.2872 Top-1 准确率: 0.6310 Top-5 准确率: 0.8955\n",
      "val 损失: 1.6316 Top-1 准确率: 0.5624 Top-5 准确率: 0.8430\n",
      "\n",
      "第 15/49 轮训练\n",
      "----------\n",
      "train 损失: 1.2363 Top-1 准确率: 0.6465 Top-5 准确率: 0.9014\n",
      "val 损失: 2.2401 Top-1 准确率: 0.4567 Top-5 准确率: 0.7611\n",
      "\n",
      "第 16/49 轮训练\n",
      "----------\n",
      "train 损失: 1.1911 Top-1 准确率: 0.6617 Top-5 准确率: 0.9067\n",
      "val 损失: 1.4135 Top-1 准确率: 0.6080 Top-5 准确率: 0.8703\n",
      "\n",
      "第 17/49 轮训练\n",
      "----------\n",
      "train 损失: 1.1465 Top-1 准确率: 0.6682 Top-5 准确率: 0.9135\n",
      "val 损失: 1.8196 Top-1 准确率: 0.5320 Top-5 准确率: 0.8234\n",
      "\n",
      "第 18/49 轮训练\n",
      "----------\n",
      "train 损失: 1.0932 Top-1 准确率: 0.6821 Top-5 准确率: 0.9198\n",
      "val 损失: 1.5531 Top-1 准确率: 0.5842 Top-5 准确率: 0.8551\n",
      "\n",
      "第 19/49 轮训练\n",
      "----------\n",
      "train 损失: 1.0585 Top-1 准确率: 0.6928 Top-5 准确率: 0.9234\n",
      "val 损失: 1.4119 Top-1 准确率: 0.6144 Top-5 准确率: 0.8750\n",
      "\n",
      "第 20/49 轮训练\n",
      "----------\n",
      "train 损失: 1.0288 Top-1 准确率: 0.7029 Top-5 准确率: 0.9285\n",
      "val 损失: 1.6177 Top-1 准确率: 0.5704 Top-5 准确率: 0.8534\n",
      "\n",
      "第 21/49 轮训练\n",
      "----------\n",
      "train 损失: 0.9985 Top-1 准确率: 0.7102 Top-5 准确率: 0.9319\n",
      "val 损失: 1.5992 Top-1 准确率: 0.5819 Top-5 准确率: 0.8555\n",
      "\n",
      "第 22/49 轮训练\n",
      "----------\n",
      "train 损失: 0.9632 Top-1 准确率: 0.7195 Top-5 准确率: 0.9351\n",
      "val 损失: 1.5132 Top-1 准确率: 0.5954 Top-5 准确率: 0.8680\n",
      "\n",
      "第 23/49 轮训练\n",
      "----------\n",
      "train 损失: 0.9223 Top-1 准确率: 0.7326 Top-5 准确率: 0.9393\n",
      "val 损失: 1.4040 Top-1 准确率: 0.6204 Top-5 准确率: 0.8777\n",
      "\n",
      "第 24/49 轮训练\n",
      "----------\n",
      "train 损失: 0.8938 Top-1 准确率: 0.7371 Top-5 准确率: 0.9447\n",
      "val 损失: 1.3674 Top-1 准确率: 0.6308 Top-5 准确率: 0.8859\n",
      "\n",
      "第 25/49 轮训练\n",
      "----------\n",
      "train 损失: 0.8642 Top-1 准确率: 0.7478 Top-5 准确率: 0.9460\n",
      "val 损失: 1.3529 Top-1 准确率: 0.6429 Top-5 准确率: 0.8877\n",
      "\n",
      "第 26/49 轮训练\n",
      "----------\n",
      "train 损失: 0.8417 Top-1 准确率: 0.7512 Top-5 准确率: 0.9493\n",
      "val 损失: 1.4000 Top-1 准确率: 0.6281 Top-5 准确率: 0.8840\n",
      "\n",
      "第 27/49 轮训练\n",
      "----------\n",
      "train 损失: 0.8122 Top-1 准确率: 0.7605 Top-5 准确率: 0.9519\n",
      "val 损失: 1.4835 Top-1 准确率: 0.6204 Top-5 准确率: 0.8649\n",
      "\n",
      "第 28/49 轮训练\n",
      "----------\n",
      "train 损失: 0.7831 Top-1 准确率: 0.7703 Top-5 准确率: 0.9553\n",
      "val 损失: 1.3714 Top-1 准确率: 0.6355 Top-5 准确率: 0.8820\n",
      "\n",
      "第 29/49 轮训练\n",
      "----------\n",
      "train 损失: 0.7594 Top-1 准确率: 0.7774 Top-5 准确率: 0.9585\n",
      "val 损失: 1.3999 Top-1 准确率: 0.6319 Top-5 准确率: 0.8775\n",
      "\n",
      "第 30/49 轮训练\n",
      "----------\n",
      "train 损失: 0.7376 Top-1 准确率: 0.7831 Top-5 准确率: 0.9602\n",
      "val 损失: 1.3576 Top-1 准确率: 0.6412 Top-5 准确率: 0.8873\n",
      "\n",
      "第 31/49 轮训练\n",
      "----------\n",
      "train 损失: 0.7149 Top-1 准确率: 0.7877 Top-5 准确率: 0.9628\n",
      "val 损失: 1.4076 Top-1 准确率: 0.6306 Top-5 准确率: 0.8782\n",
      "\n",
      "第 32/49 轮训练\n",
      "----------\n",
      "train 损失: 0.6926 Top-1 准确率: 0.7958 Top-5 准确率: 0.9656\n",
      "val 损失: 1.4116 Top-1 准确率: 0.6312 Top-5 准确率: 0.8817\n",
      "\n",
      "第 33/49 轮训练\n",
      "----------\n",
      "train 损失: 0.6660 Top-1 准确率: 0.8026 Top-5 准确率: 0.9668\n",
      "val 损失: 1.4079 Top-1 准确率: 0.6405 Top-5 准确率: 0.8866\n",
      "\n",
      "第 34/49 轮训练\n",
      "----------\n",
      "train 损失: 0.6452 Top-1 准确率: 0.8067 Top-5 准确率: 0.9694\n",
      "val 损失: 1.4776 Top-1 准确率: 0.6232 Top-5 准确率: 0.8782\n",
      "\n",
      "第 35/49 轮训练\n",
      "----------\n",
      "train 损失: 0.6274 Top-1 准确率: 0.8153 Top-5 准确率: 0.9703\n",
      "val 损失: 1.3798 Top-1 准确率: 0.6489 Top-5 准确率: 0.8863\n",
      "\n",
      "第 36/49 轮训练\n",
      "----------\n",
      "train 损失: 0.5971 Top-1 准确率: 0.8240 Top-5 准确率: 0.9739\n",
      "val 损失: 1.2972 Top-1 准确率: 0.6625 Top-5 准确率: 0.8968\n",
      "\n",
      "第 37/49 轮训练\n",
      "----------\n",
      "train 损失: 0.5734 Top-1 准确率: 0.8310 Top-5 准确率: 0.9748\n",
      "val 损失: 1.3103 Top-1 准确率: 0.6616 Top-5 准确率: 0.8940\n",
      "\n",
      "第 38/49 轮训练\n",
      "----------\n",
      "train 损失: 0.5570 Top-1 准确率: 0.8345 Top-5 准确率: 0.9773\n",
      "val 损失: 1.2849 Top-1 准确率: 0.6667 Top-5 准确率: 0.9011\n",
      "\n",
      "第 39/49 轮训练\n",
      "----------\n",
      "train 损失: 0.5446 Top-1 准确率: 0.8395 Top-5 准确率: 0.9776\n",
      "val 损失: 1.2483 Top-1 准确率: 0.6797 Top-5 准确率: 0.8996\n",
      "\n",
      "第 40/49 轮训练\n",
      "----------\n",
      "train 损失: 0.5365 Top-1 准确率: 0.8419 Top-5 准确率: 0.9781\n",
      "val 损失: 1.4296 Top-1 准确率: 0.6354 Top-5 准确率: 0.8821\n",
      "\n",
      "第 41/49 轮训练\n",
      "----------\n",
      "train 损失: 0.5193 Top-1 准确率: 0.8455 Top-5 准确率: 0.9802\n",
      "val 损失: 1.3326 Top-1 准确率: 0.6602 Top-5 准确率: 0.8975\n",
      "\n",
      "第 42/49 轮训练\n",
      "----------\n",
      "train 损失: 0.5044 Top-1 准确率: 0.8518 Top-5 准确率: 0.9801\n",
      "val 损失: 1.3779 Top-1 准确率: 0.6522 Top-5 准确率: 0.8878\n",
      "\n",
      "第 43/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4901 Top-1 准确率: 0.8541 Top-5 准确率: 0.9821\n",
      "val 损失: 1.4157 Top-1 准确率: 0.6472 Top-5 准确率: 0.8852\n",
      "\n",
      "第 44/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4768 Top-1 准确率: 0.8603 Top-5 准确率: 0.9833\n",
      "val 损失: 1.2424 Top-1 准确率: 0.6805 Top-5 准确率: 0.9071\n",
      "\n",
      "第 45/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4569 Top-1 准确率: 0.8642 Top-5 准确率: 0.9844\n",
      "val 损失: 1.4478 Top-1 准确率: 0.6424 Top-5 准确率: 0.8867\n",
      "\n",
      "第 46/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4480 Top-1 准确率: 0.8678 Top-5 准确率: 0.9850\n",
      "val 损失: 1.3610 Top-1 准确率: 0.6585 Top-5 准确率: 0.8974\n",
      "\n",
      "第 47/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4420 Top-1 准确率: 0.8696 Top-5 准确率: 0.9847\n",
      "val 损失: 1.3636 Top-1 准确率: 0.6538 Top-5 准确率: 0.8965\n",
      "\n",
      "第 48/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4192 Top-1 准确率: 0.8767 Top-5 准确率: 0.9862\n",
      "val 损失: 1.2643 Top-1 准确率: 0.6834 Top-5 准确率: 0.9098\n",
      "\n",
      "第 49/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4089 Top-1 准确率: 0.8801 Top-5 准确率: 0.9866\n",
      "val 损失: 1.3318 Top-1 准确率: 0.6674 Top-5 准确率: 0.8959\n",
      "\n",
      "训练完成，耗时 111m 34s\n",
      "最佳验证集准确率: 0.683400\n"
     ]
    }
   ],
   "source": [
    "model,(train_losses, train_top1_accs, train_top5_accs, val_losses, val_top1_accs, val_top5_accs) = train_model(net,trainloader,device,testloader, criterion, optimizer, scheduler, num_epochs)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_cpu_numpy(tensor):\n",
    "    return tensor.cpu().numpy()\n",
    "def to_cpu(list_):\n",
    "    if isinstance(list_, list):\n",
    "        return list(map(to_cpu_numpy,list_))\n",
    "    else:\n",
    "        return to_cpu_numpy(list_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 35757 (\\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from current font.\n",
      "  \n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 32451 (\\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from current font.\n",
      "  \n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 39564 (\\N{CJK UNIFIED IDEOGRAPH-9A8C}) missing from current font.\n",
      "  \n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 35777 (\\N{CJK UNIFIED IDEOGRAPH-8BC1}) missing from current font.\n",
      "  \n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font.\n",
      "  \n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  \n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 26354 (\\N{CJK UNIFIED IDEOGRAPH-66F2}) missing from current font.\n",
      "  \n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from current font.\n",
      "  \n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from current font.\n",
      "  \n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from current font.\n",
      "  \n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
      "  \n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 35757 (\\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 32451 (\\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 39564 (\\N{CJK UNIFIED IDEOGRAPH-9A8C}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 35777 (\\N{CJK UNIFIED IDEOGRAPH-8BC1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26354 (\\N{CJK UNIFIED IDEOGRAPH-66F2}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training_results(train_losses, to_cpu(train_top1_accs), to_cpu(train_top5_accs), val_losses, to_cpu(val_top1_accs), to_cpu(val_top5_accs))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
